Trained model creation method for performing specific function for electronic device, trained model for performing same function, exclusive chip and operation method for the same, and electronic device and system using the same

ABSTRACT

A learning model creation method for performing a specific function for an electronic device, according to an embodiment of the present invention, can include the steps of: preparing big data for training an artificial neural network including, in pairs, sensing data received from a random sensing data generation unit for sensing human behaviors and specific function performance determination data for determining whether to perform a specific function of an electronic device with respect to the sensing data; preparing an artificial neural network model, which includes nodes of an input layer through which the sensing data is inputted, nodes of an output layer through which the specific function performance determination data of the electronic device is outputted, and association parameters between the nodes of the input layer and the nodes of the output layer, and calculates inputs of the sensing data for the nodes of the input layer in order to output the specific function performance determination data from the nodes of the output layer; and repeatedly performing a process of inputting the sensing data included in the prepared big data into the nodes of the input layer and outputting the specific function performance determination data that pairs with the sensing data included in the big data from the nodes of the output layer so as to update the association parameters, thereby mechanically training the artificial neural network model.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.17/366,042 filed on Jul. 2, 2021, which is a continuation ofInternational Application No. PCT/KR2019/012420 filed on Sep. 24, 2019,which claims the benefit of priority to Korean Application(s) No.10-2019-0001406 filed on Jan. 4, 2019 and No. 10-2019-0002220 filed onJan. 8, 2019 in the Korean Intellectual Property Office.

BACKGROUND OF THE DISCLOSURE Technical Field

The present disclosure relates to a trained model creation method forperforming a specific function for an electronic device, a trained modelfor performing a specific function for an electronic device, a dedicatedchip for performing a specific function for an electronic device, anoperation method for a dedicated chip for performing a specific functionfor an electronic device, an electronic device having a function ofperforming a specific function, and a system for performing a specificfunction of an electronic device, and more particularly, to a trainedmodel creation method for performing a specific function for anelectronic device, a trained model for performing a specific functionfor an electronic device, a dedicated chip for performing a specificfunction for an electronic device, an operation method for a dedicatedchip for performing a specific function for an electronic device, anelectronic device having a function of performing a specific function,and a system for performing a specific function of an electronic devicefor performing a specific function, which is fast and accurate, using amodel which is trained in advance using an artificial neural network foran electronic device.

Background Art

In the case of an electronic device such as a smart phone, power for allthe unused hardware components is turned on even when the user does notuse them, thereby a lot of power consumption is caused.

To solve this problem, there has been an effort to reduce unnecessarypower consumption by turning off the power for the unused hardwarecomponents when the user does not use them.

In spite of this effort, according to a specific function performingsystem of a contemporary art, there is a technical limit in that asensor cannot precisely recognize sensing data so that a specificfunction is performed in a situation in which the sensing data does notneed to be sensed or a specific function is not performed even in asituation in which the sensing data needs to be sensed.

SUMMARY OF THE DISCLOSURE

The present disclosure is directed to solving the above-mentionedproblem and an object of the present disclosure is to perform a specificfunction in an exact situation intended by a user by preciselyunderstanding the sensing data.

Further, an object is to more quickly and precisely output determinationdata of a specific function to be performed, by inputting sensing datato an AI recognition model.

Further, an object is to promote the convenience of users only byperforming an inference process by an AI recognition model withoutperforming separate learning whenever real-time sensing data isinputted, using a previously trained AI recognition model to outputdetermination data of a specific function to be performed.

Finally, it is advantageous in that the power is not always turned on,but the system is driven only when specific sensing data is received toreduce power consumption.

One aspect of the present disclosure provides a trained model creationmethod for performing a specific function for an electronic device,including: preparing big data for training an artificial neural networkincluding, in pairs, sensing data received from a random sensing datageneration unit for sensing human behaviors and determination data ofperforming specific function for determining whether to perform aspecific function of an electronic device with respect to the sensingdata; preparing an artificial neural network model, which includes nodesof an input layer through which the sensing data is inputted, nodes ofan output layer through which the determination data of performingspecific function of the electronic device is outputted, and associatedparameters between the nodes of the input layer and the nodes of theoutput layer and calculates inputs of the sensing data for the nodes ofthe input layer in order to output the determination data of performingspecific function from the nodes of the output layer; and repeatedlyperforming a process of inputting the sensing data included in theprepared big data into the nodes of the input layer and outputting thedetermination data of performing specific function that pairs with thesensing data included in the big data from the nodes of the output layerso as to update the associated parameters, thereby mechanically trainingthe artificial neural network model.

Another aspect of the present disclosure provides a trained model forperforming a specific function for an electronic device which isacquired by mechanically training an artificial neural network model,which includes nodes of an input layer through which the sensing data isinputted, nodes of an output layer through which the determination dataof performing specific function is outputted, and associated parametersbetween the nodes of the input layer and the nodes of the output layerand calculates inputs of the sensing data for the nodes of the inputlayer in order to output the determination data of performing specificfunction from the nodes of the output layer, by repeatedly performing aprocess of inputting the sensing data included in the big data into thenodes of the input layer and outputting the determination data ofperforming specific function that pairs with the sensing data includedin the big data from the nodes of the output layer so as to update theassociated parameters, using big data for training an artificial neuralnetwork including, in pairs, sensing data received from a random sensingdata generation unit for sensing human behaviors and determination dataof performing specific function for determining whether to perform aspecific function of an electronic device with respect to the sensingdata.

Another aspect of the present disclosure provides a dedicated chip forperforming a specific function for an electronic device, including: asensing data receiving unit which receives sensing data for sensinghuman behaviors from at least one sensing data generation unit; adetermination data of performing specific function output unit whichoutputs determination data of performing specific function fordetermining whether to perform a specific function of the electronicdevice including the at least one sensing data generation unit bymatching the sensing data; and an artificial intelligence (AI)recognition model which outputs the determination data of performingspecific function in response to the input of the sensing data, in whichin the AI recognition model, a trained model is embedded, the trainedmodel is generated using an artificial neural network model, whichincludes nodes of an input layer through which the sensing data isinputted, nodes of an output layer through which the determination dataof performing specific function is outputted, and associated parametersbetween the nodes of the input layer and the nodes of the output layerand calculates inputs of the sensing data for the nodes of the inputlayer in order to output the determination data of performing specificfunction from the nodes of the output layer, and the associatedparameters are updated by repeatedly performing a process of inputtingthe sensing data included in the big data into the nodes of the inputlayer and outputting the determination data of performing specificfunction included in the big data that pairs with the sensing dataincluded in the big data from the nodes of the output layer tomechanically train the artificial neural network model.

Another aspect of the present disclosure provides a driving method of adedicated chip for performing a specific function for an electronicdevice, including: receiving sensing data for sensing human behaviorsfrom at least one sensing data generation unit; and outputtingdetermination data of performing specific function for determiningwhether to perform a specific function of the electronic deviceincluding the at least one sensing data generation unit by matching thesensing data using an AI recognition model, in which the AI recognitionmodel is configured such that a trained model is embedded in a dedicatedchip of performing a specific function, the trained model includes nodesof an input layer through which the sensing data is inputted, nodes ofan output layer through which the determination data of performingspecific function is outputted, and associated parameters between thenodes of the input layer and the nodes of the output layer, and isgenerated using an artificial neural network model which outputs thedetermination data of performing specific function from the nodes of theoutput layer in response to input of the sensing data for the nodes ofthe input layer, and the associated parameters are updated by repeatedlyperforming a process of inputting the sensing data into the nodes of theinput layer and outputting the determination data of performing specificfunction that pairs with the sensing data from the nodes of the outputlayer to mechanically train the artificial neural network model.

Another aspect of the present disclosure provides an electronic device,including: at least one sensing data generation unit which generatessensing data for sensing human behaviors; a processor which outputsdetermination data of performing specific function to determine whetherto perform a specific function of the electronic device by matching thesensing data received from the at least one sensing data generationunit; and a control unit which receives a signal to perform a specificfunction generated based on the determination data of performingspecific function from the processor to generate a driving command todrive the electronic device, in which the processor includes anartificial intelligence (AI) recognition model to output thedetermination data of performing specific function in response to theinput of the sensing data,

In the AI recognition model, a trained model is embedded, and thetrained model is generated using an artificial neural network model,which includes nodes of an input layer through which the sensing data isinputted, nodes of an output layer through which the determination dataof performing specific function is outputted, and associated parametersbetween the nodes of the input layer and the nodes of the output layer,and outputs the determination data of performing specific function fromthe nodes of the output layer in response to the input of the sensingdata for the nodes of the input layer, and the associated parameters areupdated by repeatedly performing a process of inputting the sensing dataincluded in the big data into the nodes of the input layer andoutputting the determination data of performing specific functionincluded in the big data that pairs with the sensing data included inthe big data from the nodes of the output layer to mechanically trainthe artificial neural network model.

Another aspect of the present disclosure provides a driving method of anelectronic device, including: generating sensing data for sensing humanbehaviors, in at least one sensing data generation unit; outputtingdetermination data of performing specific function to determine whetherto perform a specific function of the electronic device by matching thesensing data received from the at least one sensing data generationunit, through an AI recognition model embedded in the electronic device,in a processor, generating a signal to perform a specific function basedon the determination data of performing specific function, in theprocessor; and generating a driving command to drive the electronicdevice by receiving the signal to perform a specific function from theprocessor, in a control unit, in which, in the AI recognition model, atrained model is embedded in the electronic device, and the trainedmodel is generated using an artificial neural network model, whichincludes nodes of an input layer through which the sensing data isinputted, nodes of an output layer through which the determination dataof performing specific function is outputted, and associated parametersbetween the nodes of the input layer and the nodes of the output layer,and outputs the determination data of performing specific function fromthe nodes of the output layer in response to the input of the sensingdata for the nodes of the input layer, and the associated parameters areupdated by repeatedly performing a process of inputting the sensing dataincluded in the big data into the nodes of the input layer andoutputting the determination data of performing specific functionincluded in the big data that matches the sensing data included in thebig data from the nodes of the output layer to mechanically train theartificial neural network model.

Another aspect of the present disclosure provides an electronic devicewhich communicates with a server, including: at least one sensing datageneration unit which generates sensing data for sensing humanbehaviors; a processor which receives the sensing data from the sensingdata generation unit; a communication unit which transmits the sensingdata received from the processor to the server; a control unit whichgenerates a control command to control the electronic device; and asecond function unit which is driven based on the control command, inwhich the server outputs determination data of performing specificfunction for determining whether to perform a specific function of theelectronic device by matching the sensing data through an artificialintelligence (AI) recognition model, in the AI recognition model, atrained model is embedded in the server, the trained model is generatedusing an artificial neural network model, which includes nodes of aninput layer through which the sensing data is inputted, nodes of anoutput layer through which the determination data of performing specificfunction is outputted, and associated parameters between the nodes ofthe input layer and the nodes of the output layer, and outputs thedetermination data of performing specific function from the nodes of theoutput layer in response to the input of the sensing data for the nodesof the input layer, and the associated parameters are updated byrepeatedly performing a process of inputting the sensing data includedin the big data into the nodes of the input layer and outputting thedetermination data of performing specific function included in the bigdata that pairs with the sensing data included in the big data from thenodes of the output layer to mechanically train the artificial neuralnetwork model, the processor receives the determination data ofperforming specific function from the server to generate a signal toperform a specific function based on the determination data ofperforming specific function, the control unit generates a drivingcommand to drive the second function unit, based on the signal toperform a specific function received from the processor, and the secondfunction unit is driven based on the driving command.

According to an exemplary embodiment of the present disclosure, sensingdata is precisely understood to perform a specific function in an exactsituation intended by a user.

Further, the sensing data is inputted to an AI recognition model tooutput faster and more exact determination data of a specific functionto be performed.

Further, the convenience of users may be promoted only by performing aninference process by means of an AI recognition model, withoutperforming a separate learning whenever real-time sensing data isinputted, using a previously trained AI recognition model to outputdetermination data of a specific function to be performed.

Finally, it is advantageous in that power is not always turned on andthat, to reduce power consumption, the system is driven only whenspecific sensing data is received.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a conceptual view for explaining a system for performing aspecific function of an electronic device based on an artificial neuralnetwork according to an exemplary embodiment of the present disclosure.

FIG. 1B is a view referenced to explain a combination of sensing dataand determination data of performing specific function.

FIG. 2 is a flowchart for explaining a trained model creation method forperforming a specific function for an electronic device according to anexemplary embodiment of the present disclosure.

FIG. 3 is a block diagram for explaining a performing specific functiondetermination dedicated chip or dedicated system according to anexemplary embodiment of the present disclosure.

FIG. 4 is a flowchart for explaining an operation of a dedicated chip ofperforming a specific function according to an exemplary embodiment ofthe present disclosure.

FIG. 5 is a block diagram illustrating an electronic device according toan exemplary embodiment of the present disclosure.

FIG. 6 is a flowchart for explaining an operation of an electronicdevice according to an exemplary embodiment of the present disclosure.

FIG. 7 is a block diagram illustrating an electronic device whichcommunicates with a server according to an exemplary embodiment of thepresent disclosure.

FIG. 8 is a flowchart for explaining an operation of an electronicdevice which communicates with a server according to an exemplaryembodiment of the present disclosure.

FIGS. 9 to 15 are views referenced to explain a specific functionperforming systems by type of electronic device.

DETAILED DESCRIPTION OF THE DISCLOSURE

The present disclosure will be described in detail with reference to theaccompanying drawings based on a specific exemplary embodiment in whichthe present disclosure may be carried out as an example. The exemplaryembodiment will be described in detail enough to carry out the presentdisclosure by those skilled in the art. It should be understood thatvarious exemplary embodiments of the present disclosure are differentfrom each other, but need not be mutually exclusive. For example, aspecific figure, a structure, and a characteristic described herein maybe implemented as another exemplary embodiment without departing from aspirit and a scope of the present disclosure in relation to an exemplaryembodiment. Further, it should be understood that a position or aplacement of an individual element in each disclosed exemplaryembodiment may be changed without departing from the spirit and thescope of the present disclosure. Accordingly, a detailed descriptionbelow is not taken as a limited meaning, and the scope of the presentdisclosure is defined only by the accompanying claims together with allequivalent scopes to the claims if the scope of the present disclosureis appropriately described. Like reference numerals in the drawingdenote the same or similar function throughout several aspects.

Embodiment 1 (Electronic Device which Performs Specific Function Basedon Sensing Information about Human Behavior) Example 1-1 System forPerforming Specific Function of Electronic Device Based on ArtificialNeural Network (FIG. 1A)

Hereinafter, a specific function performing system of an electronicdevice based on an artificial neural network according to an exemplaryembodiment of the present disclosure will be described with reference tothe accompanying drawings.

FIG. 1A is a conceptual view for explaining a system 1 for performing aspecific function of an electronic device based on an artificial neuralnetwork according to an exemplary embodiment of the present disclosure.

As illustrated in FIG. 1A, the system 1 for performing a specificfunction of an electronic device based on an artificial neural networkmay include a machine learning device 2 and an electronic device 3.

The machine learning device 2 performs machine learning on an artificialneural network model 220 using big data B to generate a trained model220′. Specifically, the big data B and the artificial neural networkmodel 220 are prepared and the artificial neural network model 220 isrepeatedly mechanically trained using the big data B to generate thetrained model 220′.

The big data B according to the exemplary embodiment of the presentdisclosure may include sensing data BS and determination data ofperforming specific function BD. The sensing data BS is data generatedfrom a random sensing data generation unit and may include voiceinformation, proximity information, image information, and positioninformation, but is not limited thereto in the present exemplaryembodiment. The determination data of performing specific function BDmay be prepared in advance to be paired with the sensing data BS todetermine whether to perform a specific function of the electronicdevice 3.

The artificial neural network model 220 according to the exemplaryembodiment of the present disclosure may include nodes 221 of an inputlayer through which the sensing data BS is inputted, nodes 223 of anoutput layer through which the determination data of performing specificfunction BD is outputted, and nodes 225 of a hidden layer between thenodes 221 of the input layer and the nodes 223 of the output layer, anda plurality of associated parameters (or weights) between the nodes 223of the output layer and the nodes 221 of the input layer.

The nodes 221 of the input layer are nodes which configure the inputlayer and receive predetermined input data from the outside and thenodes 223 of the output layer are nodes which configure the output layerand output predetermined output data to the outside. The hidden nodes225 disposed between the nodes 221 of the input layer and the nodes 223of the output layer are nodes which configure the hidden layer andconnect the output data of the nodes 221 of the input layer to the inputdata of the nodes 223 of the output layer. Though FIG. 1A shows only onehidden layer, according to an exemplary embodiment, there may be aplurality of hidden layers, for example, two or four or more hiddenlayers, disposed between the input layer and the output layer toimplement a deep artificial neural network.

Each node 221 of the input layer may be fully connected or incompletelyconnected to the nodes 223 of the output layer, as illustrated in FIG.1A, depending on a structure of the artificial neural network model.

The nodes 221 of the input layer serve to receive and calculate inputdata from the outside and then transmit a result value to the hiddennode 225. The hidden node 225 also calculates the transmitted data andthen transmits the result value to a next hidden layer or output layer.Finally, data transmitted to the output layer node becomes output dataof the entire artificial neural network. When the calculation betweenthe layers of the artificial neural network is performed, apredetermined associated parameter (or a weight w) is multiplied withinput data which is inputted to a node of the corresponding layer toperform the calculation. After adding all result values (weighted sum)of a calculation performed in each node (usually, a matrix product or aconvolution product is used), predetermined output data is generated bypassing through a predetermined activation function and then transmittedto a next layer.

The activation function may usually use one of a step function, a signfunction, a linear function, a logistic sigmoid function, a hypertangent function, a ReLU function, and a softmax function. Theactivation function is appropriately selected when a structure of anartificial neural network model suitable for an application field isdesigned.

The artificial neural network is machine-trained by a process ofrepeatedly updating (or modifying) all associated parameters w in theneural network to an appropriate value. The machine learning method ofthe artificial neural network representatively includes supervisedlearning and unsupervised learning.

The supervised learning is a learning method of updating associatedparameters w to make output data obtained by inputting the input datainto the neural network similar to target data in a state in whichtarget output data which is desired to be calculated for input data byan arbitrary neural network is clearly defined. The multilayeredstructure of FIG. 1A is generated based on the supervised learning.

The unsupervised learning is a learning method that outputs consistentoutput data for similar input data without defining target data to becalculated for input data by an arbitrary neural network. Arepresentative neural network which performs the unsupervised learningincludes a self-organizing feature map (SOM) and a Boltzmann machine.

Referring to FIG. 1A again, sensing data BS included in the big data Bis inputted to the input layer of the artificial neural network model220.

For example, when first sensing data BS1 is inputted to the input layer,first element to fourth element BS1-1 to BS1-4 which configure the firstsensing data BS1 are inputted to nodes 221 of four input layers of theinput layer, respectively, and first element to third element BD1-1 toBD1-3 which configure first determination data of performing specificfunction BD1 may be outputted from nodes 223 of three output layers ofthe output layer, respectively. Here, an output of the node of oneoutput layer may include information indicating whether to perform onespecific operation. For example, the number of nodes of an output layerincluding information indicating whether to apply an entire system poweris one, and the value may be represented by 0 and 1. However, asdescribed above, the scope of the present disclosure is not limited tothe number of nodes 221 of the input layer and nodes 223 of the outputlayer illustrated in FIG. 1A.

Second sensing data BS2 and third sensing data BS3 may also be inputtedto the input layer by the similar/same method as the input method of thefirst sensing data BS1 and second determination data of performingspecific function BD2 and third determination data of performingspecific function BD3 may be outputted by the similar/same method as theoutput method of the first determination data of performing specificfunction BD1.

A combination of the sensing data BS and the determination data ofperforming specific function BD will be described with reference to FIG.1B. For example, the first determination data of performing specificfunction BD1 corresponding to the first sensing data BS1 is outputted toindicate a state in which performing specific function is available (o),and the second determination data of performing specific function BD2corresponding to the second sensing data BS2 and the third determinationdata of performing specific function BD3 corresponding to the thirdsensing data BS3 may be outputted to indicate a state in whichperforming specific function is not available (x).

As described above, the machine learning device 2 consistently andrepeatedly performs a process of inputting the sensing data BS into thenodes 221 of the input layer which configure the artificial neuralnetwork model 220 and outputting determination data of performingspecific function BD from the nodes 223 of the output layer and performsmachine learning to update an associated parameter w during this processto train the artificial neural network model 220. The machine learningdevice 2 updates the associated parameter by repeatedly performing aprocess of inputting the sensing data BS included in the big data B intothe nodes 221 of the input layer and outputting determination data ofperforming specific function BD included in the big data B that matchesthe sensing data BS from the nodes 223 of the output layer tomechanically train the artificial neural network model 220.

The trained model 220′ created in the machine learning device 2 isutilized to allow the electronic device 3 to perform a specific functionin the electronic device 3.

The electronic device 3 includes various devices which may be drivenwith an input signal to perform a specific function, such as a smartdevice including a smart phone, a computer, a home appliance, or avehicle. In the present exemplary embodiment, the electronic device isnot limited to a specific electronic device.

In the present disclosure, ‘performing specific function’ indicates thatthe electronic device 3 recognizes a call of the user to turn off afirst mode such as a stop mode, a sleep mode, or a lock mode and startsan operation in a second mode such as a booting mode, an activationmode, or an unlock mode. The first mode includes a stop mode, a sleepmode, a lock mode, and the like and includes a state in which allfunctions of the electronic device 3 are inactivated or only some of thefunctions (for example, a first function unit in FIG. 1A) is activated.The second mode includes a booting mode, an activation mode, and aunlock mode and includes a state in which all functions of theelectronic device 3 are activated or in which an inactivated function(for example, a second function unit in FIG. 1A) is activated. Forexample, the performing specific function may refer to wake-up of theentire system of the electronic device 3, but the scope of the presentdisclosure is not limited.

The electronic device 3 may include a sensing data generation unit 310,a specific function performing processor 320, a control unit 330, afirst function unit 350, a second function unit 360, and a power sourceunit 370.

The sensing data generation unit 310 includes a microphone 311, a cameraunit 312, and an infrared sensor 313, as well as an acceleration sensor,a motion sensor, a photo sensor, a heart rate sensor, a fingerprintrecognition sensor, and the like. The sensing data generation unit 310may generate voice data, image data, proximity data, motion data,location data, and fingerprint recognition data. At least one ofgenerated sensing data BS may be transmitted to the specific functionperforming processor 320.

The specific function performing processor 320 includes a computingdevice (not illustrated) which operates an artificial neural network andthe artificial neural network computing device (not illustrated)performs an operation requested by an artificial intelligence (AI)recognition model 322 and may be implemented by a general purposeprocessor or a dedicated AI acceleration processor such as CPU/GPU. Thatis, the AI recognition model 322 is configured that the trained model220′ created in the machine learning device 2 is embedded in anartificial neural network computing device in the specific functionperforming processor 320.

The specific function performing processor 320 receives sensing data RSfrom the sensing data generation unit 310 and inputs the receivedsensing data RS to a previously prepared AI recognition model 322 tooutput determination data of performing specific function RD from the AIrecognition model 322. A signal to perform a specific function generatedbased on the output determination data of performing specific functionRD is inputted to the control unit 330 to allow the electronic device 3to perform the specific function under the control of the control unit330. A detailed operation of the specific function performing processor320 will be described with reference to FIG. 3.

In the meantime, according to the present disclosure, it is illustratedthat the trained model is prepared in the machine learning device 2 andthe electronic device 3 acquires the trained model to output thedetermination data of performing specific function BD in accordance withthe input of the sensing data BS in the AI recognition model embedded inthe electronic device 3 to perform a specific function. However,according to another exemplary embodiment, additional machine learningmay be implemented to perform based on the AI recognition model of theelectronic device 3.

The control unit 330 controls an overall operation of the electronicdevice 3. For example, the control unit may include an applicationprocessor (AP), a CPU, or the like.

The control unit 330 receives a signal to perform a specific functionfrom the specific function performing processor 320 to operate theelectronic device 3.

The first function unit 350 is an always-on module which is alwaysturned on even in a state in which the power of the electronic device 2is turned off, and may include a communication unit 380, for example,when the electronic device 3 is a communication device.

The second function unit 360 is a driver which is driven in accordancewith a control command of the control unit 330 and may include an outputunit such as a display. The first function unit 350 needs to be alwaysturned on. However, the second function unit 360 may be implemented tobe normally turned off to reduce the power consumption and perform afunction only when a control command of the control unit 330 isreceived.

The power source unit 370 supplies a power to the electronic device 3.Even though the electronic device 3 is turned off, the first functionunit 350 is always supplied with the power from the power source unit370. In contrast, the second function unit 360 is normally turned offand when a control command is received from the control unit 330, may besupplied with the power from the power source unit 370.

1-1-1 Trained Model Creation Method for Performing Specific Function forElectronic Device (Independent Claim 1, FIG. 2)

FIG. 2 is a flowchart for explaining a trained model creation method forperforming a specific function for an electronic device 3 according toan exemplary embodiment of the present disclosure.

As illustrated in FIG. 2, the trained model 220′ for performing aspecific function may be created by preparing big data B includingsensing data BS and determination data of performing specific functionBD (S210), preparing an artificial neural network model 220 (S220), andmechanically repeatedly inputting sensing data BS and outputtingdetermination data of performing specific function BD using theartificial neural network model 220 (S230).

Specifically, big data B including, in pairs, sensing data BS receivedfrom at least one sensing data generation unit 310 for sensing thepresence of a human or a specific behavior and determination data ofperforming specific function BD, which matches the sensing data BS, fordetermining whether to perform a specific function of the electronicdevice 3 is prepared.

The artificial neural network model 220 including nodes 221 of the inputlayer through which the sensing data BS is inputted, nodes 223 of theoutput layer through which the determination data of performing specificfunction BD is outputted, and associated parameters between the nodes221 of the input layer and the nodes 223 of the output layer isprepared. The artificial neural network model 220 may output thedetermination data of performing specific function BD from the nodes 223of the output layer in response to the inputs of the sensing data BS forthe nodes 221 of the input layer.

The machine learning device 2 repeatedly performs the machine learningto input the sensing data BS into the nodes 221 of the input layer andoutput the determination data of performing specific function BD, whichmatches the sensing data BS, from the nodes 223 of the output layer withrespect to a large amount of sensing data BS (BS1, BS2, . . . ) includedin the big data B and a large amount of determination data of performingspecific function BD (BD1, BD2, . . . ) matching thereto, to update theassociated parameter. The machine learning is performed on theartificial neural network model 220 to create the trained model 220′configured by the updated associated parameter.

1-1-2 Trained Model

A trained model for performing a specific function for an electronicdevice 3 according to an exemplary embodiment of the present disclosurewill be described with reference to FIG. 1A.

The trained model for performing a specific function for an electronicdevice 3 according to the exemplary embodiment of the present disclosuremay be acquired by mechanically and repeatedly training the artificialneural network model 220 using the big data B including the sensing dataBS and the determination data of performing specific function BD.

Specifically, the artificial neural network model 220 includes the nodes221 of the input layer through which the sensing data BS is inputted,the nodes 223 of the output layer through which the determination dataof performing specific function BD is outputted, and the associatedparameters between the nodes 221 of the input layer and the nodes 223 ofthe output layer and may output the determination data of performingspecific function BD from the nodes 223 of the output layer in responseto the input of the sensing data BS for the nodes 221 of the inputlayer.

The machine learning device 2 updates the associated parameters byrepeatedly performing a process of inputting the sensing data BS intothe nodes 221 of the input layer and outputting the determination dataof performing specific function BD, which matches the sensing data BS,from the nodes 223 of the output layer to mechanically train theartificial neural network mode 220, thereby acquiring the trained model220′.

1-1-3 Dedicated Chip for Performing Specific Function for ElectronicDevice (Independent Claim 2, FIG. 3)

The dedicated chip 4 for performing a specific function includes an AIrecognition model 322′ embedded based on the trained model 220′ createdby performing machine learning in the machine learning device 2. Thededicated chip 4 for performing a specific function is connected to theelectronic device 3 to input sensing data RS received from theelectronic device 3 into the AI recognition model 322′ and outputdetermination data of performing specific function RD matching thesensing data RS from the AI recognition model 322′.

FIG. 3 is a block diagram for explaining a dedicated chip 4 forperforming a specific function.

As illustrated in FIG. 3, the dedicated chip 4 for performing a specificfunction for an electronic device 3 according to the exemplaryembodiment of the present disclosure may include a sensing datareceiving unit 321′, an AI recognition model 322′, and a specificfunction performance determination data output unit 323′.

The sensing data BS receiving unit 321′ receives the sensing data RSfrom the sensing data generation unit 310 of the electronic device 3 totransmit the sensing data to the AI recognition model 322′.

The AI recognition model 322′ may be embedded based on the trained model220′ which is created in advance using the artificial neural networkmodel 220 including the nodes 221 of the input layer through which thesensing data BS is inputted, the nodes 223 of the output layer throughwhich the determination data of performing specific function BD isoutputted, and the associated parameters between the nodes 221 of theinput layer and the nodes 223 of the output layer. The artificial neuralnetwork model 220 may output the determination data of performingspecific function BD from the nodes 223 of the output layer in responseto the input of the sensing data BS for the nodes 221 of the inputlayer. The trained model 220′ implemented by the associated parameterupdated by updating the associated parameters by repeatedly performing aprocess of inputting the sensing data BS into the nodes 221 of the inputlayer and outputting the determination data of performing specificfunction BD matching the sensing data BS from the nodes 223 of theoutput layer may be created. Further, the AI recognition model 322′based on the trained model 220′ may be embedded in the dedicated chip 4for performing a specific function.

The AI recognition model 322′ which is provided in advance in thededicated chip 4 for performing a specific function receives the sensingdata RS from the sensing data receiving unit 321′ to output thedetermination data of performing specific function RD matching thesensing data RS through the specific function performance determinationdata output unit 323′.

1-1-4 Learning Method of Dedicated Chip for Performing Specific Functionfor Electronic Device (Independent Claim 4, FIG. 4)

The dedicated chip 4 for performing a specific function is connected tothe electronic device 3 to input sensing data RS received from theelectronic device 3 to the AI recognition model 322′ to outputdetermination data of performing specific function RD matching thesensing data RS from the AI recognition model 322′. FIG. 4 is aflowchart for explaining an operation of a dedicated chip 4 forperforming a specific function.

As illustrated in FIG. 4, the dedicated chip 4 for performing a specificfunction may receive the sensing data RS from the sensing datageneration unit 310 of the electronic device 3 (S410). Further, thededicated chip 4 for performing a specific function may output thedetermination data of performing specific function RD for determiningwhether to perform the specific function of the electronic device 3 bymatching the sensing data RS (S420). Here, the determination data ofperforming specific function RD which matches the sensing data RS may beoutputted based on the artificial intelligence (AI) recognition model322′.

The AI recognition model 322′ is configured such that the trained model220′ may be embedded in the dedicated chip 4 for performing a specificfunction.

Specifically, the AI recognition model 322′ may be embedded based on thetrained model 220′ which is created in advance using the artificialneural network model 220 including the nodes 221 of the input layerthrough which the sensing data BS is inputted, the nodes 223 of theoutput layer through which the determination data of performing specificfunction BD is outputted, and the associated parameters between thenodes 221 of the input layer and the nodes 223 of the output layer. Theartificial neural network model 220 may output the determination data ofperforming specific function BD from the nodes 223 of the output layerin response to the input of the sensing data BS for the nodes 221 of theinput layer. The trained model 220′ implemented by the associatedparameter updated by updating the associated parameters by repeatedlyperforming a process of inputting the sensing data BS into the nodes 221of the input layer and outputting the determination data of performingspecific function BD matching the sensing data BS from the nodes 223 ofthe output layer may be created. Further, the AI recognition model 322′based on the trained model 220′ may be embedded in the dedicated chip 4for performing a specific function.

Here, the orders of generating the sensing data BS, generating thedetermination data of performing specific function BD, and forming theartificial neural network model 220 are not limited. That is, theartificial neural network model 220 may be formed after generating thedata or the data may be generated after forming the artificial neuralnetwork model 220, or the processes may be simultaneously performed.

Further, the dedicated chip 4 for performing a specific function maydetermine whether the determination data of performing specific functionis equal to or higher than a predetermined threshold by comparing thedetermination data of performing specific function acquired as a resultof the machine learning with reference specific function performing datawhich is stored in advance in the storage unit 340 included in theelectronic device 3 to be described below and when it is determined thatthe determination data of performing specific function is equal to orhigher than a predetermined threshold, generate a signal for allowingthe electronic device 3 to perform a specific function.

1-1-5 Electronic Device (Independent Claim 5, FIG. 5)

86 The electronic device 3 generates the determination data ofperforming specific function RD which matches the sensing data RS usingthe sensing data RS and may perform a specific function based on thegenerated determination data of performing specific function RD.

FIG. 5 is a block diagram for explaining the electronic device 3.

As illustrated in FIG. 5, the electronic device 3 according to theexemplary embodiment of the present disclosure may include a sensingdata generation unit 310, a specific function performing processor 320,a control unit 330, and a storage unit 340. The electronic device 3according to the exemplary embodiment of the present disclosure mayfurther include a power source unit 370, a first function unit 350, anda second function unit 360. When the above description of the exemplaryembodiment is applied to any of individual configurations and functions,the description thereof will be omitted.

The sensing data generation unit 310 may generate voice data, imagedata, position data, fingerprint recognition data from a microphone 311,a camera unit 312, and an infrared sensor 313, as well as anacceleration sensor, a motion sensor, a photo sensor, a heart ratesensor, and a fingerprint recognition sensor. At least one of generatedsensing data may be transmitted to the specific function performingprocessor 320.

The specific function performing processor 320 may include a sensingdata receiving unit 321, a trained model 322, a specific functionperformance determination data output unit 323, a specific functionperformance signal generating unit 325, and a specific functionperformance signal transmitting unit 326.

The sensing data receiving unit 321 may receive the sensing data RS fromthe sensing data generation unit 310 to transmit the sensing data to theAI recognition model 322.

The AI recognition model 322 may be embedded based on the trained model220′ which is created in advance using the artificial neural networkmodel 220 including the nodes 221 of the input layer through which thesensing data BS is inputted, the nodes 223 of the output layer throughwhich the determination data of performing specific function BD isoutputted, and the associated parameters between the nodes 221 of theinput layer and the nodes 223 of the output layer. The artificial neuralnetwork model 220 may output the determination data of performingspecific function BD from the nodes 223 of the output layer in responseto the input of the sensing data BS for the nodes 221 of the inputlayer. The trained model 220′ implemented by the associated parameterupdated by updating the associated parameters by repeatedly performing aprocess of inputting the sensing data BS into the nodes 221 of the inputlayer and outputting the determination data of performing specificfunction BD which forms a pair with the sensing data BS from the nodes223 of the output layer may be created. Further, the AI recognitionmodel 322′ based on the trained model 220′ may be embedded in thespecific function performing processor 320.

The determination data of performing specific function RD may includeinformation for determining whether to perform a specific function ofthe electronic device 3 and the information may be outputted through thespecific function performance determination data output unit 323.

The specific function performance signal generating unit 325 maygenerate a signal to perform a specific function based on thedetermination data of performing specific function RD. For example, thedetermination data of performing specific function RD is compared withreference specific function performing data including contents about apredetermined threshold which is stored in advance in the storage unit340 and when it is determined that the determination data of performingspecific function RD is equal to or higher than the predeterminedthreshold, the signal to perform a specific function may be generated.

The generated signal to perform a specific function may be transmittedto the control unit 330 by means of the specific function performancesignal transmitting unit 326. By doing this, the control unit 330controls the electronic device 3 to perform a specific function based onthe signal to perform a specific function.

The control unit 330 controls an overall operation of the electronicdevice 3. For example, the control unit may include an applicationprocessor (AP), a CPU, or the like.

The control unit 330 receives a signal to perform a specific functionfrom the specific function performing processor 320 to generate adriving command to drive the electronic device 3.

The storage unit 340 may store the data of the electronic device 3. Thestorage unit 340 may store all processing results of the specificfunction performing processor 320 and the control unit 330. That is, thespecific function performing processor 320 and the control unit 330 mayshare the same storage unit 340. However, according to another exemplaryembodiment, the specific function performing processor 320 and thecontrol unit 330 may use separate storage units.

The storage unit 340 may further store a driving command of theelectronic device 3 generated by the control unit 330.

The storage unit 340 may previously store information about referencespecific function performing data including contents about apredetermined threshold to generate a signal to perform a specificfunction. The specific function performing processor 320 may generatethe signal to perform a specific function by referring to informationabout the reference specific function performing data.

According to another exemplary embodiment, the specific functionperforming processor 320 may be implemented to further include acorrection unit (not illustrated) and a learning unit (not illustrated).By doing this, when the output determination data of performing specificfunction includes error data, corrected data obtained by correcting theerror data is generated and the corrected data is machine-learned by theAI recognition model 322 to manufacture an AI recognition model 322 withimproved precision.

For example, when a user inputs specific voice information and resultdata in which the voice information is not sensed is outputted by thespecific function performance determination data output unit 323, thecorrection unit (not illustrated) may output corrected data obtained bycorrecting the result data. The user may transmit feedback informationindicating that the output result data includes an error to thecorrection unit (not illustrated) and the correction unit (notillustrated) may generate corrected data based on the feedbackinformation. The corrected data is transmitted to the learning unit (notillustrated) to be transmitted to the nodes of the input layer of the AIrecognition model 322 and the determination data of performing specificfunction which is outputted in advance from the specific functionperforming processor 320 is outputted through the nodes of the outputlayer of the AI recognition model 322 so that the AI recognition model322 may perform the machine learning.

A model (not illustrated) with an improved precision is acquired as themachine learning result of the AI recognition model 322 and thedetermination data of performing specific function output through themodel (not illustrated) with the improved precision may contribute togenerating a signal to perform a specific function in a state with theimproved precision.

Independent Claim 6—Driving Method of Electronic Device (FIG. 6)

FIG. 6 is a flowchart for explaining a driving method of the electronicdevice 3 described in FIG. 5.

As illustrated in FIG. 6, when the sensing data generation unit 310generates sensing data BS for sensing human behavior (S610), thespecific function performing processor 320 may receive the sensing dataRS from the sensing data generation unit 310 and output thedetermination data of performing specific function RD for determiningwhether the electronic device 3 performs a specific function by pairingwith the received sensing data RS through the AI recognition model 322(S620). Here, the AI recognition model 322 is as described above in FIG.5.

The specific function performing processor 320 determines whether toperform the specific function based on the determination data ofperforming specific function RD (S630) and generates a signal to performa specific function based on the result of determining whether toperform a specific function to transmit the signal to perform a specificfunction to the control unit 330 (S640). For example, the determinationdata of performing specific function RD is compared with referencespecific function performing data including contents about apredetermined threshold which is stored in advance in the storage unit340 and when it is determined that the determination data of performingspecific function RD is equal to or higher than the predeterminedthreshold, the signal to perform a specific function may be generatedand transmitted to the control unit 330.

The control unit 330 receives the signal to perform a specific functionfrom the specific function performing processor 320 to generate adriving command to drive the electronic device 3. The electronic device3 is driven in accordance with the corresponding command (S650).

1-1-6 System for Performing Specific Function for Electronic Device

Independent Claim 7—Electronic Device Communicating with Server (FIG. 7)

FIG. 7 is a block diagram for explaining an electronic device 3 whichcommunicates with a server 5 according to an exemplary embodiment of thepresent disclosure. When the above description of the exemplaryembodiment is applied to any of individual configurations and functions,the description thereof will be omitted.

As illustrated in FIG. 7, the electronic device 3 which communicateswith a server 5 may include a sensing data generation unit 310, aspecific function performing processor 320, a control unit 330, astorage unit 340, and a communication unit 380.

The sensing data generation unit 310 performs the same function as thesensing data generation unit 310 illustrated in FIG. 5.

The specific function performing processor 320 receives sensing data RSfrom the sensing data generation unit 310 to transmit the sensing datato the communication unit 380.

The communication unit 380 transmits the sensing data RS received fromthe specific function performing processor 320 to the server 5. Further,the communication unit receives the determination data of performingspecific function RD from the server 5 to transmit the data to thespecific function performing processor 320.

The specific function performing processor 320 may generate a signal toperform a specific function based on the determination data ofperforming specific function RD received from the server 5. For example,the specific function performing processor 320 compares thedetermination data of performing specific function RD with referencespecific function performing data including contents about apredetermined threshold which is stored in advance in the storage unit340 and when it is determined that the determination data of performingspecific function RD is equal to or higher than the predeterminedthreshold, generates the signal to perform a specific function.

The storage unit 340 and the control unit 330 may be implemented in thesame/similar way to the storage unit 340 and the control unit 330described in FIG. 5.

The server 5 may include a communication module 510 and a controller522.

The communication module 510 receives the sensing data RS from thecommunication unit 380 to transmit the sensing data to the controller522. Further, the communication module 510 transmits the determinationdata of performing specific function RD outputted from the controller522 to the communication unit 380.

The controller 522 may output determination data of performing specificfunction RD for determining whether to perform a specific function ofthe electronic device 3 through the artificial intelligence (AI)recognition model 520 by matching the sensing data RS.

The AI recognition model 520 may be embedded based on the trained model220′ which is created in advance using the artificial neural networkmodel 220 including the nodes 221 of the input layer through which thesensing data BS is inputted, the nodes 223 of the output layer throughwhich the determination data of performing specific function BD isoutputted, and the associated parameters between the nodes 221 of theinput layer and the nodes 223 of the output layer. The artificial neuralnetwork model 220 may output the determination data of performingspecific function BD from the nodes 223 of the output layer in responseto the input of the sensing data BS for the nodes 221 of the inputlayer. The trained model 220′ implemented by the associated parameterupdated by updating the associated parameters by repeatedly performing aprocess of inputting the sensing data BS into the nodes 221 of the inputlayer and outputting the determination data of performing specificfunction BD, which pairs with the sensing data BS, from the nodes 223 ofthe output layer may be created. Further, the AI recognition model 520based on the trained model 220′ may be embedded in the controller 522.

The controller 522 transmits the determination data of performingspecific function BD outputted from the AI recognition model 520 to theelectronic device 3 via the communication module 510. The specificfunction performing processor 320 of the electronic device 3 maygenerate a signal to perform a specific function based on thedetermination data of performing specific function RD received from theserver 5 to operate the second function unit 360.

Independent Claim 8—Driving Method of Electronic Device Communicatingwith Server (FIG. 8)

FIG. 8 is a flowchart for a driving method of an electronic device 3which communicates with the server 5 according to the exemplaryembodiment of the present disclosure.

As illustrated in FIG. 8, the electronic device 3 may generate sensingdata RS for sensing human behavior. The electronic device may transmitthe generated sensing data RS to the server 5. The server 5 may receivethe sensing data RS to output determination data of performing specificfunction RD for determining whether to perform a specific function ofthe electronic device 3 through the AI recognition model 520 by matchingthe sensing data RS.

The AI recognition model 520 performs the same function as the AIrecognition model 520 described above in FIG. 7.

The server 5 may transmit the output determination data of performingspecific function RD to the electronic device 3.

The electronic device 3 receives the determination data of performingspecific function RD from the server 5 to generate a signal for allowingthe electronic device 3 to perform a specific function based on thedetermination data of performing specific function. Further, theelectronic device 3 may be driven based on the signal to perform aspecific function.

Modified Example 1-1-7 System for Performing Specific Function ofElectronic Device and Activating Specific Function

As described above, the electronic device 3 of the present disclosureincludes various devices which may be driven with an input signal toperform a specific function, such as a smart device including a smartphone, a computer, a home appliance, or a vehicle. In the presentexemplary embodiment, it is not limited to a specific electronic device.

Further, as described above, in the present disclosure, ‘performingspecific function’ indicates that the electronic device 3 recognizescall of the user to turn off a first mode such as a stop mode, a sleepmode, or a lock mode and start an operation in a second mode, such as abooting mode, an activation mode, or an unlock mode. The first modeincludes a stop mode, a sleep mode, and a lock mode and includes a statein which all functions of the electronic device 3 are inactivated oronly some of the functions (for example, a first function unit in FIG.1A) is activated. The second mode includes a booting mode, an activationmode, and a unlock mode and includes a state in which all functions ofthe electronic device 3 are activated or an inactivated function (forexample, a second function unit in FIG. 1A) is activated.

Hereinafter, a system for performing a specific function of anelectronic device 3 and activating a specific function will be describedwith reference to FIGS. 9 to 15.

As illustrated in FIG. 9, when the electronic device 3 is a smart phone,the smart phone generates determination data of performing specificfunction of the smart phone which matches the sensing data BS using theembedded AI recognition model 322 based on the sensing data BS. Further,the smart phone generates the signal to perform a specific functionbased on the determination data of performing specific function so thatthe smart phone may perform the specific function.

For example, the smart phone generates determination data of performingspecific function such as unlock data, booting data, sleep mode offdata, voice assistant call data, music play data, volume control (up ordown) data, or screen brightness control (up or down) data of the smartphone which matches the voice data using the embedded AI recognitionmodel 322. Further, the signal to perform a specific function isgenerated based on the determination data of performing specificfunction so that the first mode of the smart phone is turned off and thesmart phone may operate in a second mode.

Further, the smart phone may generate unlock determination data of thesmart phone which matches the image data using the embedded AIrecognition model 322. Further, a unlock signal is generated based onthe unlock determination data so that the smart phone turns off the lockmode and operates in an unlock mode. (FIG. 9 (a)->(b))

Further, the smart phone generates determination data of performingspecific function such as unlock determination data of the smart phoneor various authentication data which matches fingerprint recognitiondata, using the embedded AI recognition model 322. Further, the signalto perform a specific function is generated based on the determinationdata of performing specific function so that the smart phone turns offthe first mode and may operate in a second mode.

Similarly, as illustrated in FIG. 10, when the electronic device 3 is acomputer (for example, a tablet, a notebook, or a PC), the computergenerates determination data of performing specific function of thecomputer which matches the sensing data BS using the embedded AIrecognition model 322 based on the sensing data BS. Further, the signalto perform a specific function is generated based on the determinationdata of performing specific function so that the computer may performthe specific function. In the exemplary embodiment of the presentdisclosure, the sensing data BS may include voice data, infrared sensorsensing data, image data, and fingerprint recognition data.

For example, the computer generates determination data of performingspecific function such as unlock data, booting data, sleep mode offdata, voice assistant call data, music play data, camera activationdata, volume control (up or down) data, or screen brightness control (upor down) data of the computer which matches the voice data using theembedded AI recognition model 322. Further, the signal to perform aspecific function is generated based on the determination data ofperforming specific function so that the computer turns off the firstmode and may operate in a second mode. FIG. 10 (a)->(b) illustrates thatthe computer turns off a stop mode and may operate in a booting mode.

Further, the computer may generate unlock determination data of thecomputer which matches the image data using the embedded AI recognitionmodel 322. Further, the unlock signal is generated based on the unlockdetermination data so that the computer turns off the lock mode andoperates in an unlock mode.

Further, the computer generates determination data of performingspecific function such as unlock determination data of the computer orvarious authentication data which matches fingerprint recognition data,using the embedded AI recognition model 322. Further, the signal toperform a specific function is generated based on the determination dataof performing specific function so that the computer turns off a firstmode and may operate in a second mode.

As illustrated in FIG. 11, when the electronic device 3 is a homeappliance, the home appliance generates determination data of performingspecific function which matches the sensing data BS using the embeddedAI recognition model 322 based on the sensing data BS. Further, the homeappliance generates the signal to perform a specific function based onthe determination data of performing specific function so that the homeappliance may perform the specific function. In the exemplary embodimentof the present disclosure, the sensing data BS may include voice data,infrared sensor sensing data, image data, fingerprint recognition data,and the like.

For example, a refrigerator generates data for determining whether adisplay is on, which matches the voice data, using the embedded AIrecognition model 322. Further, a display-on signal is generated basedon the data for determining whether a display is on so that a displaydevice of the refrigerator is turned on. (FIG. 11 (a)->(b))

Further, the refrigerator may generate determination data of performingspecific function such as unlock determination data of the refrigeratoror various authentication data which matches fingerprint recognitiondata, using the embedded AI recognition model 322. Further, the signalto perform a specific function is generated based on the determinationdata of performing specific function so that the refrigerator turns offa first mode and may operate in a second mode.

For example, a TV generates a determination data of performing specificfunction including data for determining whether a TV is on, channelcontrol data, and volume control data, which matches the voice data,using the embedded AI recognition model 322. Further, the signal toperform a specific function is generated based on the determination dataof performing specific function so that the TV turns off the first modeand may operate in a second mode. FIG. 13 (a)->(b) illustrate that theTV turns off an A channel 100 mode and operates in a B channel 200 mode.

For example, an air conditioner generates data for determining a blowingdirection of cool air, which matches the image data, using the embeddedAI recognition model 322. Here, the image data refers to data acquiredby tracking a user using a camera mounted in the air conditioner. Ablowing signal is generated based on the data for determining a blowingdirection of cool air so that as illustrated in FIG. 14, the A directionblowing mode of the air conditioner is turned off and the airconditioner operates in a B direction blowing mode.

As illustrated in FIG. 12, when the electronic device 3 is a vehicle,the vehicle generates determination data of performing specific functionof the vehicle, which matches the sensing data BS, using the embedded AIrecognition model 322 based on the sensing data BS. Further, the signalto perform a specific function is generated based on the determinationdata of performing specific function of the vehicle to perform aspecific function of the vehicle. In the exemplary embodiment of thepresent disclosure, the sensing data BS may include voice data, infraredsensor sensing data, image data, fingerprint recognition data, and thelike.

For example, the vehicle generates determination data of performingspecific function including unlock of the vehicle, variousauthentication, and engine start, which matches the fingerprintrecognition data, using the embedded AI recognition model 322. Further,the signal to perform a specific function is generated based on thedetermination data of performing specific function so that the firstmode of the vehicle is turned off and the vehicle may operate in asecond mode. FIG. 12 illustrates that the vehicle verifies a user'sfingerprint to operate in an engine start mode (b) from an engine offmode (a).

For example, the vehicle may generate a signal to perform a specificfunction based on determination data of performing specific functionsuch as rear window heater on/off, front window defroster on/off, airconditioner/heater (including a handle/seat heater) on/off, wiperon/off, high beam/various lights on/off, emergency light on/off,music/radio on/off, and volume control, navigation call, voice assistantcall, driving mode change, start-up, or gear shift, which matches thevoice data, using the embedded AI recognition model 322, so that a firstmode of the vehicle is turned off and the vehicle may operate in asecond mode.

Specifically, when the electronic device 3 is a vehicle, a microphonewhich is installed in the vehicle (or separately attached to thevehicle) recognizes a voice command of the user to operate a heater inthe vehicle, operate wipers, play music, or operate the air conditionerusing the embedded AI recognition model 322 to allow the vehicle toperform predetermined additional functions. The additional function maybe performed only by simply transmitting a voice command to the vehiclewhile the user drives a vehicle so that the user may focus on thedriving without averting the vision elsewhere to prevent the accident.

Further, it is possible to implement that not only the vehicle alwaysrecognizes the voice command of the user to perform the predeterminedaddition function, but also, according to another exemplary embodiment,the vehicle recognizes the voice command of the user after the userpushes a separate button (mounted on a handle or a seat) to perform thepredetermined additional function. In the former case, the voice commandof the user is always recognized so that it is more convenient. However,when there is a noise in the vehicle, there may be a misrecognitionproblem due to the nose so that in the latter case, only when the buttonis pushed, the voice command of the user excluding the noise may bebetter recognized. Therefore, it is advantageous in that the voicecommand may be conveniently recognized even in the noisy environment. Asillustrated in FIG. 15, when the electronic device 3 is an illuminationdevice, the illumination device generates illumination device on/offdetermination data, which matches the sensing data BS, using theembedded AI recognition model 322 based on the sensing data BS. Anillumination device on/off signal is generated based on the illuminationdevice on/off determination data to turn on/off the illumination device.According to the exemplary embodiment of the present disclosure, thesensing data BS includes voice data, image data, and the like.

For example, FIG. 15 illustrates that the illumination device recognizesa user's voice so that the illumination device operates by changing anoff-state (a) to an on-state (b).

However, the present disclosure is not limited to the electronic device3 described in FIGS. 9 to 15 but the contents of FIGS. 9 to 15 may beapplied to every type of device including a controller which performsarithmetic functions in the same/similar way.

For reference, the contents about the sensing data and the determinationdata of performing specific function for the electronic device 3described in FIGS. 9 to 15 are merely an example, and the scope of thepresent disclosure is not limited thereto and may be applied to anothertype of sensing data and determination data of performing specificfunction in the same/similar way.

The contents about the mode change of the electronic device 3 describedin FIGS. 9 to 15 may be implemented by applying 1-1-1 Trained modelcreation method for performing specific function for electronic device,1-1-2 Trained model, 1-1-3 Dedicated chip for performing specificfunction for electronic device, 1-1-4 Operation method of dedicated chipfor performing specific function for electronic device, 1-1-5 Electronicdevice, and 1-1-6 System for performing specific function for electronicdevice in the same/similar way.

Embodiment 2 (Electronic Device which Performs Specific Function Basedon Human Voice Information)

Hereinafter, a system of allowing an electronic device 3 to perform aspecific function based on human voice information according to anotherexemplary embodiment of the present disclosure will be described.

Example 2-1 Trained Model System for Unlocking Locked Smart Phone Basedon Voice Data

Specifically, a trained model system for unlocking a locked smart phonebased on voice data according to another exemplary embodiment of thepresent disclosure will be described.

The description of the system 1 for performing a specific function foran electronic device based on the artificial neural network describedabove in FIG. 1A may be applied to the trained model system forunlocking the locked smart phone based on the voice data according toanother exemplary embodiment of the present disclosure in thesame/similar way.

For example, the trained model system for unlocking the locked smartphone based on the voice data according to another exemplary embodimentof the present disclosure uses the voice data as the sensing data BS,uses the smart phone as the electronic device 3 and uses data fordetermining whether the smart phone is unlocked as the determinationdata of performing specific function BD.

The machine learning device 2 may generate the trained model 220′ byrepeatedly performing the process of inputting the voice data into thenodes 221 of the input layer and outputting the data for determiningwhether the smart phone is unlocked from the nodes 223 of the outputlayer, based on the artificial neural network model 220.

The electronic device 3 inputs the voice data to the AI recognitionmodel 322 in which the trained model 220′ is embedded to output smartphone unlock determination data from the AI recognition model 322. Thelocked smart phone may be unlocked based on the signal to perform aspecific function generated from the output smart phone unlockdetermination data.

It is advantageous in that the voice data is inputted to the AIrecognition model 322 to quickly and precisely output the smart phoneunlock determination data.

Further, it is advantageous in that the AI recognition model 322 whichis trained in advance is used so that separate learning is not performedwhenever the real time voice data is inputted, but the smart phoneunlock determination data may be automatically quickly output so thatthe convenience of the user may be promoted.

Furthermore, it is advantageous in that the power is not always turnedon, but the system is driven only when sensing data is received so thatthe power consumption may be reduced.

2-1-1 Trained Model Creation Method for Unlocking Smart Phone

The above description of Example 1-1-1 may be applied to a trained modelcreation method for unlocking a smart phone according to anotherexemplary embodiment of the present disclosure in the same/similar way.

For example, according to the trained model creation method forunlocking the locked smart phone based on the voice data according toanother exemplary embodiment of the present disclosure, the voice datais used as the sensing data BS, the smart phone is used as theelectronic device 3 and smart phone unlock determination data is used asthe determination data of performing specific function BD.

The machine learning device 2 may generate the trained model 220′ byrepeatedly performing the process of inputting the voice data into thenodes 221 of the input layer and outputting the data for determiningwhether the smart phone is unlocked from the nodes 223 of the outputlayer, based on the artificial neural network model 220.

2-1-2 Trained Model for Unlocking Smart Phone

The above description of Example 1-1-2 may be applied to a trained modelfor unlocking a smart phone according to another exemplary embodiment ofthe present disclosure in the same/similar way.

For example, the trained model for unlocking the locked smart phonebased on the voice data according to another exemplary embodiment of thepresent disclosure may be created from the artificial neural networkmodel 220 using the voice data as the sensing data BS, using the smartphone as the electronic device 3 and using data for determining whetherthe smart phone is unlocked as the determination data of performingspecific function BD.

Specifically, the machine learning device 2 may generate the trainedmodel 220′ by repeatedly performing the process of inputting the voicedata into the nodes 221 of the input layer and outputting the data fordetermining whether the smart phone is unlocked from the nodes 223 ofthe output layer, based on the artificial neural network model 220.

2-1-3 Dedicated Chip for Unlocking Smart Phone

The above description of Example 1-1-3 may be applied to a dedicatedchip for unlocking a smart phone according to another exemplaryembodiment of the present disclosure in the same/similar way.

For example, in the dedicated chip for unlocking the smart phoneaccording to another exemplary embodiment of the present disclosure, thedetermination data of performing specific function is smart phone unlockdetermination data in response to the input of the voice data to thesmart phone and the machine learning of the artificial neural networkmodel 220 is to repeatedly perform the process of inputting the voicedata into the nodes 221 of the input layer and outputting data fordetermining whether the smart phone is unlocked from the nodes 223 ofthe output layer, and the trained model 220′ created as the result ofrepeated performance may be embedded in the dedicated chip for unlockingthe smart phone as an AI recognition model 322′.

2-1-4 Smart Phone with Unlocking Function Using Artificial NeuralNetwork

The above description of Example 1-1-5 may be applied to a smart phonehaving an unlocking function using an artificial neural networkaccording to another exemplary embodiment of the present disclosure inthe same/similar way.

For example, the determination data of performing specific function isthe smart phone unlock determination data in response to the input ofthe voice data to the smart phone and the machine learning of theartificial neural network model 220 is to repeatedly perform the processof inputting the voice data into the nodes 221 of the input layer andoutputting data for determining whether the smart phone is unlocked fromthe nodes 223 of the output layer, and the trained model 220′ created asthe result of repeated performance may be embedded in the smart phone asan AI recognition model 322′.

2-1-5 Smart Phone Unlock System Using Artificial Neural Network(Server-Client Model)

The above description of Example 1-1-6 may be applied to the smart phoneunlock system using an artificial neural network according to anotherexemplary embodiment of the present disclosure in the same/similar wayand the difference will be mainly described below.

For example, the determination data of performing specific function isdata for determining whether the smart phone is unlocked in response tothe input of the voice data to the smart phone and the machine learningof the artificial neural network model 220 is to repeatedly perform theprocess of inputting the voice data into the nodes 221 of the inputlayer and outputting data for determining whether the smart phone isunlocked from the nodes 223 of the output layer, and the trained model220′ created as the result of repeated performance may be embedded inthe server 5 as an AI recognition model 322′.

Example 2-2 Trained Model System for Turning Off Sleep Mode of ComputerBased on Voice Data

Model System

A trained model system for turning off a sleep mode of a computer basedon voice data according to another exemplary embodiment of the presentdisclosure will be described.

The description of the electronic device specific function performingsystem 1 based on the artificial intelligence network described above inFIG. 1A may be applied to the trained model system for turning off asleep mode of a computer based on the voice data according to anotherexemplary embodiment of the present disclosure in the same/similar way.

For example, the trained model system for turning off a sleep mode of acomputer based on the voice data according to another exemplaryembodiment of the present disclosure uses the voice data as the sensingdata BS, uses the computer as the electronic device 3 and uses data fordetermining whether to turn off the sleep mode of the computer as thedetermination data of performing specific function BD.

The machine learning device 2 may generate the trained model 220′ byrepeatedly performing the process of inputting the voice data into thenodes 221 of the input layer and outputting the data for determiningwhether to turn off the sleep mode of the computer from the nodes 223 ofthe output layer, based on the artificial neural network model 220.

The electronic device 3 inputs the voice data to the AI recognitionmodel 322 in which the trained model 220′ is embedded to output the datafor determining whether to turn off the sleep mode of the computer fromthe AI recognition model 322.

It is advantageous in that the voice data is inputted to the AIrecognition model 322 to quickly and precisely output the data fordetermining whether to turn off the sleep mode of the computer.

Further, it is advantageous in that the AI recognition model 322 whichis trained in advance is used so that separate learning is not performedwhenever the real time voice data is inputted, but the data fordetermining whether to turn off the sleep mode of the computer may beautomatically quickly output so that the convenience of the user may bepromoted.

It is advantageous in that the power is not always turned on, but thesystem is driven only when sensing data is received so that the powerconsumption may be reduced.

2-2-1 Trained Model Creation Method for Turning Off Computer Sleep Mode

The above description of Example 1-1-1 may be applied to a trained modelcreation method for turning off a sleep mode of a computer according toanother exemplary embodiment of the present disclosure in thesame/similar way.

For example, according to the trained model creation method for turningoff a sleep mode of a computer based on the voice data according toanother exemplary embodiment of the present disclosure, the voice datais used as the sensing data BS, the computer is used as the electronicdevice 3 and data for turning off a computer sleep mode is used as thedetermination data of performing specific function BD.

The machine learning device 2 may generate the trained model 220′ byrepeatedly performing the process of inputting the voice data into thenodes 221 of the input layer and outputting the data for turning off thecomputer sleep mode from the nodes 223 of the output layer, based on theartificial neural network model 220.

2-2-2 Trained Model for Turning Off Computer Sleep Mode

The above description of Example 1-1-2 may be applied to a trained modelfor turning off a computer sleep mode according to another exemplaryembodiment of the present disclosure in the same/similar way.

For example, the trained model for turning off a computer sleep modebased on the voice data according to another exemplary embodiment of thepresent disclosure may be created from the artificial neural networkmodel 220 using the voice data as the sensing data BS, using thecomputer as the electronic device 3 and using data for turning off acomputer sleep mode as the determination data of performing specificfunction BD.

Specifically, the machine learning device 2 may generate the trainedmodel 220′ by repeatedly performing the process of inputting the voicedata into the nodes 221 of the input layer and outputting the data forturning off the computer sleep mode from the nodes 223 of the outputlayer, based on the artificial neural network model 220.

2-2-3 Dedicated Chip for Turning Off Computer Sleep Mode

The above description of Example 1-1-3 may be applied to a dedicatedchip for turning off a computer sleep mode according to anotherexemplary embodiment of the present disclosure in the same/similar way.

For example, in the dedicated chip for turning off a computer sleep modeaccording to another exemplary embodiment of the present disclosure, thedetermination data of performing specific function is data fordetermining whether to turn off the computer sleep mode in response tothe input of the voice data to the computer and the machine learning ofthe artificial neural network model 220 is to repeatedly perform theprocess of inputting the voice data into the nodes 221 of the inputlayer and outputting data for determining whether to turn off thecomputer sleep mode from the nodes 223 of the output layer, and thetrained model 220′ created as the result of repeated performance may beembedded in the dedicated chip for turning off a computer sleep mode asan AI recognition model 322′.

2-2-4 Computer with Sleep Mode Turning-Off Function Using ArtificialNeural Network

The above description of Example 1-1-5 may be applied to a computerhaving a sleep mode turning off function using an artificial neuralnetwork according to another exemplary embodiment of the presentdisclosure in the same/similar way.

For example, the determination data of performing specific function isdata for determining whether to turn off the computer sleep mode inresponse to the input of the voice data to the computer and the machinelearning of the artificial neural network model 220 is to repeatedlyperform the process of inputting the voice data into the nodes 221 ofthe input layer and outputting data for determining whether to turn offthe computer sleep mode from the nodes 223 of the output layer, and thetrained model 220′ created as the result of repeated performance may beembedded in the computer as an AI recognition model 322′.

2-2-5 System for Turning Off Sleep Mode of Computer Using ArtificialNeural Network (Server-Client Model)

The above description of Example 1-1-6 may be applied to a system forturning off a sleep mode of a computer using an artificial neuralnetwork according to another exemplary embodiment of the presentdisclosure in the same/similar way.

For example, the determination data of performing specific function isdata for determining whether to turn off the sleep mode of the computerin response to the input of the voice data to the computer and themachine learning of the artificial neural network model 220 is torepeatedly perform the process of inputting the voice data into thenodes 221 of the input layer and outputting data for determining whetherto turn off the sleep mode of the computer from the nodes 223 of theoutput layer, and the trained model 220′ created as the result ofrepeated performance may be embedded in the server 5 as an AIrecognition model 322′.

Modified Example 2-2-6 System for Booting Computer Based on VoiceInformation Using Artificial Neural Network

The above description of Example 2-2 may be applied to the system forbooting a computer based on voice information using an artificial neuralnetwork in the same/similar way. However, as the determination data ofperforming specific function, data for determining whether to boot thecomputer may be used rather than computer sleep mode off determinationdata.

Modified Example 2-2-7 Specific Function Performing System of TV Basedon Voice Information Using Artificial Neural Network

The above description of Example 2-2 may be applied to the system forperforming a specific function of a TV based on voice information usingan artificial neural network in the same/similar way. However, the TV isused as the electronic device 3 and as the determination data ofperforming specific function, data for determining whether to activatethe TV may be used rather than the computer sleep mode off determinationdata.

Example 2-3 Trained Model System for Activating Specific Function(Display) of Home Appliance (TV or Refrigerator) Based on VoiceInformation

A trained model system for turning on a display of a home appliancebased on voice data according to another exemplary embodiment of thepresent disclosure will be described.

The description of the system 1 for performing a specific function foran electronic device based on the artificial intelligence networkdescribed above in FIG. 1A may be applied to the trained model systemfor turning on a display of a home appliance based on the voice dataaccording to another exemplary embodiment of the present disclosure inthe same/similar way.

For example, the trained model system for turning on a display of a homeappliance based on the voice data according to another exemplaryembodiment of the present disclosure uses the voice data as the sensingdata BS, uses the home appliance (a TV or a refrigerator) as theelectronic device 3 and uses data for determining whether to turn on thedisplay of the home appliance as the determination data of performingspecific function BD.

The machine learning device 2 may generate the trained model 220′ byrepeatedly performing the process of inputting the voice data into thenodes 221 of the input layer and outputting the data for determiningwhether to turn on the display of the home appliance from the nodes 223of the output layer, based on the artificial neural network model 220.

The electronic device 3 inputs the voice data to the AI recognitionmodel 322 in which the trained model 220′ is embedded to output the datafor determining whether to turn on the display of the home appliancefrom the AI recognition model 322.

The voice data is inputted to the AI recognition model 322 to quicklyand precisely output the data for determining whether to turn on thedisplay.

Further, it is advantageous in that the AI recognition model 322 whichis trained in advance is used so that separate learning is not performedwhenever the real time voice data is inputted, but the data fordetermining whether to turn on the display may be automatically quicklyoutput so that the convenience of the user may be promoted.

It is advantageous in that the power is not always turned on, but thesystem is driven only when sensing data is received so that the powerconsumption may be reduced.

2-3-1 Trained Model Creation Method for Activating Display of HomeAppliance

The above description of Example 1-1-1 may be applied to a trained modelcreation method for turning on a display of a home appliance accordingto another exemplary embodiment of the present disclosure in thesame/similar way.

For example, according to the trained model creation method for turningon a display of a home appliance based on the voice data according toanother exemplary embodiment of the present disclosure, the voice datais used as the sensing data BS, the home appliance is used as theelectronic device 3 and data for determining whether to turn on thedisplay of the home appliance is used as the determination data ofperforming specific function BD.

The machine learning device 2 may generate the trained model 220′ byrepeatedly performing the process of inputting the voice data into thenodes 221 of the input layer and outputting the data for determiningwhether to turn on the display of the home appliance from the nodes 223of the output layer, based on the artificial neural network model 220.

2-3-2 Trained Model for Activating Display of Home Appliance

The above description of Example 1-1-2 may be applied to a trained modelfor turning on a display of a home appliance according to anotherexemplary embodiment of the present disclosure in the same/similar way.

For example, the trained model for turning on a display of a homeappliance according to another exemplary embodiment of the presentdisclosure may be created from the artificial neural network model 220using the voice data as the sensing data BS, using the home appliance asthe electronic device 3 and using data for determining whether to turnon a display of a home appliance as the determination data of performingspecific function BD.

Specifically, the machine learning device 2 may generate the trainedmodel 220′ by repeatedly performing the process of inputting the voicedata into the nodes 221 of the input layer and outputting the data fordetermining whether to turn on the display of the home appliance fromthe nodes 223 of the output layer, based on the artificial neuralnetwork model 220.

2-3-3 Dedicated Chip for Activating Display of Home Appliance

The above description of Example 1-1-3 may be applied to a dedicatedchip for turning on a display of a home appliance according to anotherexemplary embodiment of the present disclosure in the same/similar way.

For example, in the dedicated chip for turning on a display of a homeappliance according to another exemplary embodiment of the presentdisclosure, the determination data of performing specific function isdata for determining whether to turn on the display of the homeappliance in response to the input of the voice data to the homeappliance and the machine learning of the artificial neural networkmodel 220 is to repeatedly perform the process of inputting the voicedata into the nodes 221 of the input layer and outputting data fordetermining whether to turn on the display of the home appliance fromthe nodes 223 of the output layer, and the trained model 220′ created asthe result of repeated performance may be embedded in the dedicated chipfor turning on the display of the home appliance as an AI recognitionmodel 322′.

2-3-4 Home Appliance with Display Activating Function Using ArtificialNeural Network

The above description of Example 1-1-5 may be applied to a homeappliance having a display turning-on function using an artificialneural network according to another exemplary embodiment of the presentdisclosure in the same/similar way.

For example, the determination data of performing specific function isdata for determining whether to turn on the display of the homeappliance in response to the input of the voice data to the homeappliance and the machine learning of the artificial neural networkmodel 220 is to repeatedly perform the process of inputting the voicedata into the nodes 221 of the input layer and outputting data fordetermining whether to turn on the display of the home appliance fromthe nodes 223 of the output layer, and the trained model 220′ created asthe result of repeated performance may be embedded in the home applianceas an AI recognition model 322′.

2-3-5 System for Activating Display of Home Appliance Using ArtificialNeural Network (Server-Client Model)

The above description of Example 1-1-6 may be applied to a system foractivating a display of a home appliance using an artificial neuralnetwork according to another exemplary embodiment of the presentdisclosure in the same/similar way.

For example, the determination data of performing specific function isdata for determining whether to turn on the display of the homeappliance in response to the input of the voice data to the homeappliance and the machine learning of the artificial neural networkmodel 220 is to repeatedly perform the process of inputting the voicedata into the nodes 221 of the input layer and outputting data fordetermining whether to turn on the display of the home appliance fromthe nodes 223 of the output layer, and the trained model 220′ created asthe result of repeated performance may be embedded in the server 5 as anAI recognition model 322′.

Example 2-4 Trained Model System for Unlocking Vehicle Based on VoiceInformation

A trained model system for unlocking a vehicle based on voice dataaccording to another exemplary embodiment of the present disclosure willbe described.

The description of the system 1 for performing a specific function foran electronic device based on the artificial intelligence networkdescribed above in FIG. 1A may be applied to the trained model systemfor unlocking a vehicle based on the voice data according to anotherexemplary embodiment of the present disclosure in the same/similar way.

For example, the trained model system for unlocking the vehicle based onthe voice data according to another exemplary embodiment of the presentdisclosure uses the voice data as the sensing data BS, uses the vehicleas the electronic device 3 and uses data for determining whether thevehicle is unlocked as the determination data of performing specificfunction BD.

The machine learning device 2 may generate the trained model 220′ byrepeatedly performing the process of inputting the voice data into thenodes 221 of the input layer and outputting the data for determiningwhether the vehicle is unlocked from the nodes 223 of the output layerbased on the artificial neural network model 220.

The electronic device 3 inputs the voice data to the AI recognitionmodel 322 in which the trained model 220′ is embedded to output data fordetermining whether the vehicle is unlocked from the AI recognitionmodel 322.

It is advantageous in that the voice data is inputted to the AIrecognition model 322 to quickly and precisely output the data fordetermining whether the vehicle is unlocked.

Further, it is advantageous in that the AI recognition model 322 whichis trained in advance is used so that separate learning is not performedwhenever the real time voice data is inputted, but the data fordetermining whether the vehicle is unlocked may be automatically quicklyoutput so that the convenience of the user may be promoted.

2-4-1 Trained Model Creation Method for Unlocking Vehicle

The above description of Example 1-1-1 may be applied to a trained modelcreation method for unlocking a vehicle according to another exemplaryembodiment of the present disclosure in the same/similar way.

For example, according to the trained model creation method forunlocking the vehicle based on the voice data according to anotherexemplary embodiment of the present disclosure, the voice data is usedas the sensing data BS, the vehicle is used as the electronic device 3and data for determining whether the vehicle is unlocked is used as thedetermination data of performing specific function BD.

The machine learning device 2 may generate the trained model 220′ byrepeatedly performing the process of inputting the voice data into thenodes 221 of the input layer and outputting the data for determiningwhether the vehicle is unlocked from the nodes 223 of the output layerbased on the artificial neural network model 220.

2-4-2 Trained Model for Unlocking Vehicle

The above description of Example 1-1-2 may be applied to a trained modelfor unlocking a vehicle according to another exemplary embodiment of thepresent disclosure in the same/similar way.

For example, the trained model for unlocking a vehicle based on thevoice data according to another exemplary embodiment of the presentdisclosure may be created from the artificial neural network model 220using the voice data as the sensing data BS, using the vehicle as theelectronic device 3 and using data for determining whether the vehicleis unlocked as the determination data of performing specific functionBD.

Specifically, the machine learning device 2 may generate the trainedmodel 220′ by repeatedly performing the process of inputting the voicedata into the nodes 221 of the input layer and outputting the data fordetermining whether the vehicle is unlocked from the nodes 223 of theoutput layer, based on the artificial neural network model 220.

2-4-3 Dedicated Chip for Unlocking Vehicle

The above description of Example 1-1-3 may be applied to a dedicatedchip for determining whether the vehicle is unlocked according toanother exemplary embodiment of the present disclosure in thesame/similar way.

For example, in the dedicated chip for determining whether the vehicleis unlocked according to another exemplary embodiment of the presentdisclosure, the determination data of performing specific function isdata for determining whether the vehicle is unlocked in response to theinput of the voice data to the vehicle and the machine learning of theartificial neural network model 220 is to repeatedly perform the processof inputting the voice data into the nodes 221 of the input layer andoutputting data for determining whether the vehicle is unlocked from thenodes 223 of the output layer, and the trained model 220′ created as theresult of repeated performance may be embedded in the dedicated chip fordetermining whether the vehicle is unlocked as an AI recognition model322′.

2-4-4 Vehicle with Unlocking Function Using Artificial Neural Network

The above description of Example 1-1-5 may be applied to a vehiclehaving an unlocking function using an artificial neural networkaccording to another exemplary embodiment of the present disclosure inthe same/similar way.

For example, the determination data of performing specific function isdata for determining whether the vehicle is unlocked in response to theinput of the voice data to the vehicle and the machine learning of theartificial neural network model 220 is to repeatedly perform the processof inputting the voice data into the nodes 221 of the input layer andoutputting data for determining whether the vehicle is unlocked from thenodes 223 of the output layer, and the trained model 220′ created as theresult of repeated performance may be embedded in the vehicle as an AIrecognition model 322′.

2-4-5 System for Unlocking Vehicle Using Artificial Neural Network(Server-Client Model)

The above description of Example 1-1-6 may be applied to a system forunlocking a vehicle using an artificial neural network according toanother exemplary embodiment of the present disclosure in thesame/similar way.

For example, the determination data of performing specific function isdata for determining whether the vehicle is unlocked in response to theinput of the voice data to the vehicle and the machine learning of theartificial neural network model 220 is to repeatedly perform the processof inputting the voice data into the nodes 221 of the input layer andoutputting data for determining whether the vehicle is unlocked from thenodes 223 of the output layer, and the trained model 220′ created as theresult of repeated performance may be embedded in the server 5 as an AIrecognition model 322′.

Modified Example 2-4-6 System for Starting Engine of a Vehicle Based onVoice Information Using Artificial Neural Network

The above description of Example 2-4 may be applied to the system forstarting an engine of a vehicle based on voice information using anartificial neural network in the same/similar way. However, as thedetermination data of performing specific function, data for determiningwhether the engine of the vehicle is started may be used rather than thedata for determining whether the vehicle is unlocked.

Embodiment 3 (Electronic Device which Performs Specific Function Basedon Human Proximity Information)

Hereinafter, a system for performing a specific function of anelectronic device 3 based on human proximity information according toanother exemplary embodiment of the present disclosure will bedescribed.

Example 3-1 Trained Model System for Unlocking Smart Phone Based onSensing Data of Infrared Sensing Sensor

Specifically, a trained model system for unlocking a locked smart phonebased on sensing data of an infrared sensing sensor according to anotherexemplary embodiment of the present disclosure will be described.

The above description of the system 1 for performing a specific functionfor an electronic device based on the artificial intelligence networkdescribed above in FIG. 1A may be applied to the trained model systemfor unlocking the locked smart phone based on the sensing data of aninfrared sensing sensor according to another exemplary embodiment of thepresent disclosure in the same/similar way.

For example, the trained model system for unlocking the locked smartphone based on the sensing data of an infrared sensing sensor accordingto another exemplary embodiment of the present disclosure uses thesensing data of an infrared sensing sensor as the sensing data BS, usesthe smart phone as the electronic device 3 and uses data for determiningwhether the smart phone is unlocked as the determination data ofperforming specific function BD.

The machine learning device 2 may generate the trained model 220′ byrepeatedly performing the process of inputting the sensing data of aninfrared sensing sensor into the nodes 221 of the input layer andoutputting the data for determining whether the smart phone is unlockedfrom the nodes 223 of the output layer based on the artificial neuralnetwork model 220.

The electronic device 3 inputs the sensing data of an infrared sensingsensor to the AI recognition model 322 in which the trained model 220′is embedded to output the smart phone unlock determination data from theAI recognition model 322. The locked smart phone may be unlocked basedon the signal to perform a specific function generated from the outputsmart phone unlock determination data.

The sensing data of an infrared sensing sensor is inputted to the AIrecognition model 322 to quickly and precisely output the smart phoneunlock determination data.

Further, it is advantageous in that the AI recognition model 322 whichis trained in advance is used so that separate learning is not performedwhenever the real time sensing data of an infrared sensing sensor isinputted, but the data for determining whether the smart phone isunlocked may be automatically quickly output so that the convenience ofthe user may be promoted.

It is advantageous in that the power is not always turned on, but thesystem is driven only when sensing data is received so that the powerconsumption may be reduced.

Example 3-2 Trained Model System for Turning Off Computer Sleep ModeBased on Sensing Data of Infrared Sensing Sensor

According to a trained model system for turning off a computer sleepmode based on sensing data of an infrared sensing sensor according toanother exemplary embodiment of the present disclosure, the descriptionof Example 3-1 may be applied in the same/similar way. However, thecomputer is used as the electronic device 3 and computer sleep mode offdetermination data is used as determination data of performing specificfunction.

Modified Example 3-2-6 System of Booting Computer Based on Sensing Dataof Infrared Sensing Sensor Using Artificial Neural Network

According to the system of booting a computer based on sensing data ofan infrared sensing sensor using an artificial neural network, the abovedescription of Example 3-2 may be applied in the same/similar way.However, computer booting determination data may be used as thedetermination data of performing specific function.

Modified Example 3-2-7 System for Performing Specific Function of TVBased on Sensing Data of Infrared Sensing Sensor Using Artificial NeuralNetwork

According to the system for performing a specific function of TV basedon sensing data of an infrared sensing sensor using an artificial neuralnetwork, the above description of Example 3-2 may be applied in thesame/similar way. However, the TV is used as the electronic device 3 andTV activation determination data is used as determination data ofperforming specific function.

Example 3-3 Trained Model System for Activating Specific Function(Display) of Home Appliance (TV or Refrigerator) Based on Sensing Dataof Infrared Sensing Sensor

According to a trained model system for activating display-on of a homeappliance based on sensing data of an infrared sensing sensor accordingto another exemplary embodiment of the present disclosure, the abovedescription of Example 3-2 may be applied in the same/similar way.However, the home appliance is used as the electronic device 3 anddisplay-on determination data is used as the determination data ofperforming specific function.

Example 3-4 Trained Model System for Unlocking Vehicle Based on SensingData of Infrared Sensing Sensor

According to another exemplary embodiment of the present disclosure, theabove description of Example 3-2 may be applied in the same/similar way.However, the vehicle is used as the electronic device 3 and vehicleunlock determination data is used as the determination data ofperforming specific function.

Modified Example 3-4-6 System for Starting Engine of Vehicle Based onSensing Data of Infrared Sensing Sensor Using Artificial Neural Network

According to a system for starting an engine of a vehicle based onsensing data of an infrared sensing sensor according to anotherexemplary embodiment of the present disclosure, the above description ofExample 3-4 may be applied in the same/similar way. However, the vehicleengine start-up data may be used as the determination data of performingspecific function.

Embodiment 4 (Electronic Device which Performs Specific Function Basedon Image Information)

Hereinafter, a system for performing a specific function for anelectronic device 3 based on image information according to anotherexemplary embodiment of the present disclosure will be described.

Example 4-1 Trained Model System for Unlocking Smart Phone Based onSensing Data of Image Sensor

Specifically, a trained model system for unlocking a locked smart phonebased on image data according to another exemplary embodiment of thepresent disclosure will be described.

The above description of the system 1 for performing a specific functionfor an electronic device based on the artificial intelligence networkdescribed above in FIG. 1A may be applied to the trained model systemfor unlocking the locked smart phone based on the image data accordingto another exemplary embodiment of the present disclosure in thesame/similar way.

For example, the trained model system for unlocking the locked smartphone based on the image data according to another exemplary embodimentof the present disclosure uses the image data as the sensing data BS,uses the smart phone as the electronic device 3 and uses data fordetermining whether the smart phone is unlocked as the determinationdata of performing specific function BD. The image data may be dataacquired from an image sensor such as a camera.

The machine learning device 2 may generate the trained model 220′ byrepeatedly performing the process of inputting the image data into thenodes 221 of the input layer and outputting the data for determiningwhether the smart phone is unlocked from the nodes 223 of the outputlayer, based on the artificial neural network model 220.

The electronic device 3 inputs the image data to the AI recognitionmodel 322 in which the trained model 220′ is embedded to output smartphone unlock determination data from the AI recognition model 322. Thelocked smart phone may be unlocked based on the signal to perform aspecific function generated from the output smart phone unlockdetermination data.

It is advantageous in that the image data is inputted to the AIrecognition model 322 to quickly and precisely output the smart phoneunlock determination data.

Further, it is advantageous in that the AI recognition model 322 whichis trained in advance is used so that separate learning is not performedwhenever the real time image data is inputted, but the smart phoneunlock determination data may be automatically quickly output so thatthe convenience of the user may be promoted.

It is advantageous in that the power is not always turned on, but thesystem is driven only when sensing data is received so that the powerconsumption may be reduced.

Modified Example 4-1 Trained Model System for Activating Camera of SmartPhone Based on Sensing Data of Image Sensor

According to a trained model system for activating a camera of a smartphone based on image data according to another exemplary embodiment ofthe present disclosure, the above description of Example 4-1 may beapplied in the same/similar way. However, smart phone camera activationdetermination data may be used as the determination data of performingspecific function.

Example 4-2 Trained Model System for Turning Off Computer Sleep ModeBased on Sensing Data of Image Sensor

According to a trained model system for turning off a computer sleepmode based on image data according to another exemplary embodiment ofthe present disclosure, the description of Example 4-1 may be applied inthe same/similar way. However, the computer is used as the electronicdevice 3 and computer sleep mode off determination data is used asdetermination data of performing specific function.

Modified Example 4-2-6 System of Booting Computer Based on Sensing Dataof Image Sensor Using Artificial Neural Network

According to another exemplary embodiment of the present disclosure, theabove description of Example 4-2 may be applied in the same/similar way.However, computer booting determination data may be used asdetermination data of performing specific function.

Modified Example 4-2-7 System for Performing Specific Function of TVBased on Sensing Data of Image Sensor Using Artificial Neural Network

According to a system for performing a specific function of a TV basedon image data using an artificial neural network according to anotherexemplary embodiment of the present disclosure, the above description ofExample 4-2 may be applied in the same/similar way. However, the TV isused as the electronic device 3 and TV activation determination data isused as determination data of performing specific function.

Example 4-3 Trained Model System for Activating Specific Function(Display) of Home Appliance (TV or Refrigerator) Based on Sensing Dataof Image Sensor

According to a trained model system for activating display on of a homeappliance based on image data using an artificial neural networkaccording to another exemplary embodiment of the present disclosure, theabove description of Example 4-1 may be applied in the same/similar way.

However, the home appliance (for example, a TV or a refrigerator) isused as the electronic device 3 and display on determination data isused as determination data of performing specific function.

Example 4-4 Trained Model System for Unlocking Vehicle Based on SensingData of Image Sensor

According to a trained model system for unlocking a vehicle based onimage data using an artificial neural network according to anotherexemplary embodiment of the present disclosure, the above description ofExample 4-1 may be applied in the same/similar way. However, the vehicleis used as the electronic device 3 and vehicle unlock determination datais used as determination data of performing specific function.

Modified Example 4-4-6 System for Starting Engine of Vehicle Based onSensing Data of Image Sensor Using Artificial Neural Network

According to a trained model system for starting an engine of a vehiclebased on image data using an artificial neural network according toanother exemplary embodiment of the present disclosure, the abovedescription of Example 4-4 may be applied in the same/similar way.However, vehicle engine start-up determination data is used asdetermination data of performing specific function.

Additional Example—when Electronic Device is Vehicle, Specific Functionof Vehicle with Respect to Input of Voice Data is Performed

In the case of the additional Example, the above description of Example2-4 (trained model system for unlocking vehicle based on voiceinformation) may be applied in the same/similar way. Among them,specifically, the description of 2-4-4 (the vehicle having an unlockingfunction using an artificial neural network) may be applied in thesame/similar way.

For example, the determination data of performing specific function isdata for determining whether a rear window heater in the vehicle isoperated in response to the input of the voice data to the vehicle andthe machine learning of the artificial neural network model is torepeatedly perform a process of inputting the voice data into the nodesof the input layer and outputting data for determining whether the rearwindow heater is operated from the nodes of the output layer.

Further, the determination data of performing specific function is datafor determining whether a front window defrosting function in thevehicle is operated in response to the input of the voice data to thevehicle and the machine learning of the artificial neural network modelis to repeatedly perform a process of inputting the voice data into thenodes of the input layer and outputting data for determining whether thefront window defrosting function is operated from the nodes of theoutput layer.

Further, the determination data of performing specific function is datafor determining whether an air conditioner or a heater in the vehicle isoperated in response to the input of the voice data to the vehicle andthe machine learning of the artificial neural network model is torepeatedly perform a process of inputting the voice data into the nodesof the input layer and outputting data for determining whether the airconditioner or the heater is operated from the nodes of the outputlayer.

Further, the determination data of performing specific function is datafor determining whether a wiper in the vehicle is operated in responseto the input of the voice data to the vehicle and the machine learningof the artificial neural network model is to repeatedly perform aprocess of inputting the voice data into the nodes of the input layerand outputting data for determining whether the wiper is operated fromthe nodes of the output layer.

Further, the determination data of performing specific function is datafor determining whether an illumination device in the vehicle isoperated in response to the input of the voice data to the vehicle andthe machine learning of the artificial neural network model is torepeatedly perform a process of inputting the voice data into the nodesof the input layer and outputting data for determining whether theillumination device is operated from the nodes of the output layer.

The illumination device in the vehicle of the present disclosure mayinclude an emergency light, a high beam, and a head light, but the scopeof the present disclosure is not limited thereto.

Further, the determination data of performing specific function is datafor determining whether an AVN device in the vehicle is operated inresponse to the input of the voice data to the vehicle and the machinelearning of the artificial neural network model is to repeatedly performa process of inputting the voice data into the nodes of the input layerand outputting data for determining whether the AVN device is operatedfrom the nodes of the output layer. Depending on an exemplaryembodiment, data for determining whether the AVN device is controlled(for example, volume control) may be used as the determination data ofperforming specific function.

Further, the determination data of performing specific function is datafor determining whether a voice assistant call function in the vehicleis operated in response to the input of the voice data to the vehicleand the machine learning of the artificial neural network model is torepeatedly perform a process of inputting the voice data into the nodesof the input layer and outputting data for determining whether the voiceassistant call function is operated from the nodes of the output layer.

Further, the determination data of performing specific function is datafor determining whether a driving mode of the vehicle is changed inresponse to the input of the voice data to the vehicle and the machinelearning of the artificial neural network model is to repeatedly performa process of inputting the voice data into the nodes of the input layerand outputting the data for determining whether the driving mode ischanged from the nodes of the output layer.

Further, the determination data of performing specific function is datafor determining whether a driving mode of the vehicle is changed inresponse to the input of the voice data to the vehicle and the machinelearning of the artificial neural network model is to repeatedly performa process of inputting the voice data into the nodes of the input layerand outputting the data for determining whether the driving mode ischanged from the nodes of the output layer.

Further, the determination data of performing specific function is datafor determining whether to shift the gear of the vehicle in response tothe input of the voice data to the vehicle and the machine learning ofthe artificial neural network model is to repeatedly perform a processof inputting the voice data into the nodes of the input layer andoutputting the data for determining whether to shift the gear from thenodes of the output layer.

However, the scope of the present disclosure is not limited to theperforming specific function in the vehicle as described above, butincludes all types of specific functions which may be performed in thevehicle based on the voice command.

The features, structures, effects and the like described in theforegoing embodiments are included in one embodiment of the presentdisclosure and are not necessarily limited to one embodiment. Moreover,the features, structures, effects and the like illustrated in eachembodiment may be combined or modified by those skilled in the art forthe other embodiments to be carried out. Therefore, the combination andthe modification of the present disclosure are interpreted to beincluded within the scope of the present disclosure.

In the above description, the present disclosure has been describedbased on the exemplary embodiment, but the exemplary embodiments are forillustrative, and do not limit the present disclosure, and those skilledin the art will appreciate that various modifications and applications,which are not exemplified in the above description, may be made withoutdeparting from the scope of the essential characteristic of the presentexemplary embodiments. For example, each component described in detailin the embodiment can be modified. Further, the differences related tothe modification and the application should be construed as beingincluded in the scope of the present disclosure defined in theaccompanying claims.

According to an exemplary embodiment of the present disclosure, sensingdata is precisely understood to perform a specific function in an exactsituation intended by a user.

Further, the sensing data is inputted to an AI recognition model tooutput faster and more precise determination data of performing specificfunction.

Further, the convenience of users may be promoted only by performing aninference process by means of an AI recognition model without performinga separate learning whenever real-time sensing data is inputted using apreviously trained AI recognition model to output determination data ofperforming specific function.

Finally, the power is not always turned on, but the system is drivenonly when specific sensing data is reduced so that the power consumptionmay be reduced.

What is claimed is:
 1. An electronic device comprising: a sensing datageneration unit configured to generate at least one sensing data; adedicated artificial intelligence (AI) acceleration processor configuredto generate a wake-up data to switch from a first mode to a second modeby processing the at least one sensing data by a trained artificialneural network model trained by machine learning technique; a controlunit configured to generate a control command based on the wake-up data;and a power source unit configured to: supply power to the sensing datageneration unit and the dedicated AI acceleration processor whilesupplying no power to the control unit during the first mode; and supplypower to the sensing data generation unit, the dedicated AI accelerationprocessor, and the control unit during the second mode.
 2. Theelectronic device of claim 1, wherein the trained artificial neuralnetwork model is embedded in the dedicated AI acceleration processor. 3.The electronic device of claim 1, wherein the trained artificial neuralnetwork model is an artificial intelligence recognition model configuredto output a determination data of performing specific function inresponse to the at least one sensing data.
 4. The electronic device ofclaim 1, wherein the electronic device is one of a smart phone, acomputer, a server, a display device, a refrigerator, an airconditioner, a home appliance, a vehicle, an illumination device, and acommunication device.
 5. The electronic device of claim 1, furthercomprising: a first function unit that is an always-on module turned-oneven when the electronic device is turned-off.
 6. The electronic deviceof claim 1, further comprising a first function unit and a secondfunction unit, and wherein the first function unit is turned-on, andwherein the second function unit is turned-off to reduce powerconsumption and then is turned-on when the control command is receivedfrom the control unit.
 7. The electronic device of claim 1, wherein theat least one sensing data include one of a voice data, an image data, aposition data, a fingerprint recognition data, an infrared sensorsensing data.
 8. The electronic device of claim 1, wherein theelectronic device is configured to always recognize a voice command. 9.The electronic device of claim 1, wherein the control unit is one of aCPU or an application processor (AP) configured to control an overalloperation of the electronic device.
 10. A system for performing aspecific function, the system comprising: a dedicated artificialintelligence (AI) acceleration processor configured to generate adetermination data by using a trained artificial neural network modeltrained by machine learning technique to switch from a first mode to asecond mode by receiving at least one sensing data received from asensing data generation unit; a control unit configured to generate acontrol command based on the determination data; and a power source unitconfigured to: supply power to the sensing data generation unit and thededicated AI acceleration processor while supplying no power to thecontrol unit during the first mode; and supply power to the sensing datageneration unit, the dedicated AI acceleration processor, and thecontrol unit during the second mode.
 11. The system of claim 10, whereinthe trained artificial neural network model is embedded in the dedicatedAI acceleration processor.
 12. The system of claim 10, wherein thedetermination data is configured to perform specific function inresponse to the at least one sensing data.
 13. The system of claim 10,wherein the system is one of a smart phone, a computer, a server, adisplay device, a refrigerator, an air conditioner, a home appliance, avehicle, an illumination device, and a communication device.
 14. Thesystem of claim 10, wherein the first mode is an always-on mode evenwhen the system is turned-off.
 15. The system of claim 10, wherein ifthe system is in the first mode, the control command switches the firstmode to the second mode based on the determination data.
 16. The systemof claim 10, wherein the at least one sensing data is generated from oneof a microphone, a camera, an acceleration sensor, a motion sensor, aphoto sensor, a heart rate sensor, a fingerprint recognition data, andan infrared sensor.
 17. The system of claim 10, wherein the system isconfigured to always recognize a voice command in the first mode and thesecond mode.
 18. The system of claim 10, wherein the control unit is oneof a CPU or an application processor (AP) configured to control anoverall operation of the system.
 19. An always-on apparatus comprising:a first processor configured to generate a determination data by using atrained artificial neural network model to switch from a first mode to asecond mode by receiving at least one sensing data received from asensing data generation unit; a second processor different from thefirst processor, configured to generate a control command based on thedetermination data; and a power source unit configured to: supply powerto the sensing data generation unit and the first processor whilesupplying no power to the second processor in the first mode; and supplypower to the sensing data generation unit, the first processor, and thesecond processor in the second mode.
 20. The always-on apparatus ofclaim 19, wherein the first processor is a dedicated AI accelerationprocessor, and the second processor is a CPU.